Friday, August 4, 2017

Even though the demand for the platinum
group metals (PGM) grew roughly 10 times over the last 40 years, the price
remained the stable. This forces
organisations to focus on asset management with specific attention to the
measurement of utilisation and performance of assets.

In the article titled Asset Performance Management: Proposing a Conceptual Model to Apply OEE
to a Continuous Process a conceptual model was presented. This article follows on that and the
objective of is to test the proposed model with actual results and prove the
validity thereof.

DATA
COLLECTION

Technology improved significantly since the
days of manual data capturing and data of this importance should leverage off
of the technology improvements (Muchiri and Pintelon, 2008; Ljungberg, 1998; Dal et
al., 2000; Kullh and Josefine, 2013; Jonsson and Lesshammar, 1999). Bearing this in mind, the first set of data
needed was an indication of whether the concentrator was running or not. A single, vital piece of equipment was
identified and used for this metric. If the
mill was both running and consuming a certain amount of electricity as
indicated by the plant historian, the plant was running.

When the plant was not running, a selection
screen was presented to the operator.
The operator could then select a downtime reason from a prepopulated
list and the times were allocated to a certain bucket, as presented in Figure 1. At the end of a time period, time buckets
were aggregated and availability was calculated:

For the same time period the Actual Mill
Feed Rate used in the Performance calculation was sourced from a SCADA system
and averaged over the amount of hours that the concentrator was actually
running. This provided the actual hourly
feed rate used in equation 2. The
Average of Top 6 Hourly Feed Rates was calculated from historical data and was
is seen as the nameplate performance of the concentrator as used in equation 2:

(2)

To calculate quality, the daily activities
of metallurgists were focussed on. Each
day metallurgists monitor and adjust certain metrics to ensure a high quality
output. If all of these values are in range, metallurgists are somewhat
convinced that the quality of the product will be high. Historical data was analysed and a range for
each metric was set up. A metric scored
100% when it operated in the specified range and deviances from the range
decreased the value thereof. Table 1 lists
the scores associated with the deviances.

Data was collected over a time period north
of two years and it involved seven concentrators, but data for the first half
of 2014 were excluded from the results due to the notorious five month strike
in the industry during 2014. The collected data was then analysed, aggregated
and fed into the model presented.

It is important to note that the figures
used in this report are distorted figures due to the sake of
confidentiality. The concepts, however,
remains truthful.

Availability

Figure 2 shows
boxplots for the availability metric and confirms the heterogeneity of
concentrators. Upon inspection C7’s wide
spread was attributed to erratic breakdown patterns and a process which was not
under control.

Performance

Similar to availability, the performance
metric the performance metric confirmed the heterogeneity of concentrators and suggested
that concentrators should perform between 80 and 90%.

It is noted that one of the concentrators occasionally
performed above 100%. Although this is possible,
it is not recommended as per asset management guidelines.

Quality

Again, the quality metric confirmed the
heterogeneity of concentrators and the indication is that the quality of the
product was largely dependent on the type of ore fed into the concentrator.

OEE

Not surprisingly, OEE indicated that each
concentrator operated within a narrow band but compared to other concentrators,
the spread is wider ranging between 45 and 80%. The trend in Figure 5
clearly shows a spike in data and upon inspection, it was learned that the
performance spiked when the concentrators started up post the five month long
strike.

The previous article highlighted the
importance of engineering asset management strategies and data integrity and
importance was highlighted earlier. As
per literature, data was collected electronically, classified by human
intervention and thereafter calculated and presented in the OEE model.

The model to measure asset performance was
tested in this article. A sensible
calculation for the availability metric was used where after a new performance metric
as offered was tested. Finally, a new
way of measuring quality in mining concentrators were tested. All three metrics held true and proved to be
useful in measuring the performance of assets.

The model proposed in the previous article
offers a bird’s eye view on the concentrators and reports on the overall
effectiveness and maintainability of assets.
It is proved that the model is valid and should be used by concentrator
management to operate more effectively.

Antoine obtained his bachelor’s degree in
industrial engineering from the University of Pretoria in 2006. Post studies, he started his career in
consulting where he quickly got awarded the responsibility and accountability
for the management and delivery of large projects for key clients. Currently, he is a specialist in his field
with responsibilities ranging across the entire value chain of the mining and
processing industry. He furthermore
completed his master’s degree in engineering, with focus on Engineering
Management in 2015 and is currently working at Lonmin Plc in Marikana, South
Africa.